






from typing import Dict

import numpy as np

from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline, PipelineException


if is_tf_available():
      import tensorflow as tf

      from ..tf_utils import stable_softmax


if is_torch_available():
      import torch


SCREAMING_SNAKE_CASE__		:							Union[str, Any] 				=				logging.get_logger(__name__)







@add_end_docstrings(
    __lowerCamelCase							,      R'\n        top_k (`int`, defaults to 5):\n            The number of predictions to return.\n        targets (`str` or `List[str]`, *optional*):\n            When passed, the model will limit the scores to the passed targets instead of looking up in the whole\n            vocab. If the provided targets are not in the model vocab, they will be tokenized and the first resulting\n            token will be used (with a warning, and that might be slower).\n\n    '							,      )
class 				UpperCAmelCase_  (		__lowerCamelCase		):




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase		):
          if self.framework == "tf":
               UpperCAmelCase__      :  str				      =			tf.where(input_ids == self.tokenizer.mask_token_id		).numpy()
          elif self.framework == "pt":
               UpperCAmelCase__      :  Optional[int]				      =			torch.nonzero(input_ids == self.tokenizer.mask_token_id	,						as_tuple=_lowerCAmelCase		)
          else:
               raise ValueError("""Unsupported framework"""		)
          return masked_index




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase		):
          UpperCAmelCase__      :  Optional[int]				      =			self.get_masked_index(_lowerCAmelCase		)
          UpperCAmelCase__      :  Any				      =			np.prod(masked_index.shape		)
          if numel < 1:
               raise PipelineException(
                   """fill-mask"""	,						self.model.base_model_prefix	,						f"No mask_token ({self.tokenizer.mask_token}) found on the input"	,						)




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase		):
          if isinstance(_lowerCAmelCase	,						_lowerCAmelCase		):
               for model_input in model_inputs:
                    self._ensure_exactly_one_mask_token(model_input["""input_ids"""][0]		)
          else:
               for input_ids in model_inputs["input_ids"]:
                    self._ensure_exactly_one_mask_token(_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase=None	,						**_lowerCAmelCase		):
          if return_tensors is None:
               UpperCAmelCase__      :  Union[str, Any]				      =			self.framework
          UpperCAmelCase__      :  List[Any]				      =			self.tokenizer(_lowerCAmelCase	,						return_tensors=_lowerCAmelCase		)
          self.ensure_exactly_one_mask_token(_lowerCAmelCase		)
          return model_inputs




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase		):
          UpperCAmelCase__      :  List[str]				      =			self.model(**_lowerCAmelCase		)
          UpperCAmelCase__      :  Union[str, Any]				      =			model_inputs["""input_ids"""]
          return model_outputs




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase=5	,						_lowerCAmelCase=None		):
          # Cap top_k if there are targets
          if target_ids is not None and target_ids.shape[0] < top_k:
               UpperCAmelCase__      :  List[str]				      =			target_ids.shape[0]
          UpperCAmelCase__      :  Tuple				      =			model_outputs["""input_ids"""][0]
          UpperCAmelCase__      :  int				      =			model_outputs["""logits"""]

          if self.framework == "tf":
               UpperCAmelCase__      :  Tuple				      =			tf.where(input_ids == self.tokenizer.mask_token_id		).numpy()[:, 0]

               UpperCAmelCase__      :  List[str]				      =			outputs.numpy()

               UpperCAmelCase__      :  Optional[Any]				      =			outputs[0, masked_index, :]
               UpperCAmelCase__      :  Tuple				      =			stable_softmax(_lowerCAmelCase	,						axis=-1		)
               if target_ids is not None:
                    UpperCAmelCase__      :  Optional[Any]				      =			tf.gather_nd(tf.squeeze(_lowerCAmelCase	,						0		)	,						target_ids.reshape(-1	,						1		)		)
                    UpperCAmelCase__      :  List[str]				      =			tf.expand_dims(_lowerCAmelCase	,						0		)

               UpperCAmelCase__      :  Optional[int]				      =			tf.math.top_k(_lowerCAmelCase	,						k=_lowerCAmelCase		)
               UpperCAmelCase__      ,		UpperCAmelCase__      :  Tuple				      =			topk.values.numpy(), topk.indices.numpy()
          else:
               UpperCAmelCase__      :  Dict				      =			torch.nonzero(input_ids == self.tokenizer.mask_token_id	,						as_tuple=_lowerCAmelCase		).squeeze(-1		)
               # Fill mask pipeline supports only one ${mask_token} per sample

               UpperCAmelCase__      :  Optional[Any]				      =			outputs[0, masked_index, :]
               UpperCAmelCase__      :  Tuple				      =			logits.softmax(dim=-1		)
               if target_ids is not None:
                    UpperCAmelCase__      :  Optional[int]				      =			probs[..., target_ids]

               UpperCAmelCase__      ,		UpperCAmelCase__      :  Tuple				      =			probs.topk(_lowerCAmelCase		)

          UpperCAmelCase__      :  Optional[int]				      =			[]
          UpperCAmelCase__      :  List[Any]				      =			values.shape[0] == 1
          for i, (_values, _predictions) in enumerate(zip(values.tolist()	,						predictions.tolist()		)		):
               UpperCAmelCase__      :  Optional[Any]				      =			[]
               for v, p in zip(_values	,						_predictions		):
                    # Copy is important since we're going to modify this array in place
                    UpperCAmelCase__      :  Tuple				      =			input_ids.numpy().copy()
                    if target_ids is not None:
                         UpperCAmelCase__      :  Tuple				      =			target_ids[p].tolist()

                    UpperCAmelCase__      :  Tuple				      =			p
                    # Filter padding out:
                    UpperCAmelCase__      :  Tuple				      =			tokens[np.where(tokens != self.tokenizer.pad_token_id		)]
                    # Originally we skip special tokens to give readable output.
                    # For multi masks though, the other [MASK] would be removed otherwise
                    # making the output look odd, so we add them back
                    UpperCAmelCase__      :  Tuple				      =			self.tokenizer.decode(_lowerCAmelCase	,						skip_special_tokens=_lowerCAmelCase		)
                    UpperCAmelCase__      :  List[Any]				      =			{"""score""": v, """token""": p, """token_str""": self.tokenizer.decode([p]		), """sequence""": sequence}
                    row.append(_lowerCAmelCase		)
               result.append(_lowerCAmelCase		)
          if single_mask:
               return result[0]
          return result




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase=None		):
          if isinstance(_lowerCAmelCase	,						_lowerCAmelCase		):
               UpperCAmelCase__      :  Any				      =			[targets]
          try:
               UpperCAmelCase__      :  Optional[Any]				      =			self.tokenizer.get_vocab()
          except Exception:
               UpperCAmelCase__      :  Optional[int]				      =			{}
          UpperCAmelCase__      :  Dict				      =			[]
          for target in targets:
               UpperCAmelCase__      :  Tuple				      =			vocab.get(_lowerCAmelCase	,						_lowerCAmelCase		)
               if id_ is None:
                    UpperCAmelCase__      :  Optional[int]				      =			self.tokenizer(
                        _lowerCAmelCase	,						add_special_tokens=_lowerCAmelCase	,						return_attention_mask=_lowerCAmelCase	,						return_token_type_ids=_lowerCAmelCase	,						max_length=1	,						truncation=_lowerCAmelCase	,						)["""input_ids"""]
                    if len(_lowerCAmelCase		) == 0:
                         logger.warning(
                             f"The specified target token `{target}` does not exist in the model vocabulary. "
                             """We cannot replace it with anything meaningful, ignoring it"""		)
                         continue
                    UpperCAmelCase__      :  Tuple				      =			input_ids[0]
                    # XXX: If users encounter this pass
                    # it becomes pretty slow, so let's make sure
                    # The warning enables them to fix the input to
                    # get faster performance.
                    logger.warning(
                        f"The specified target token `{target}` does not exist in the model vocabulary. "
                        f"Replacing with `{self.tokenizer.convert_ids_to_tokens(id_		)}`."		)
               target_ids.append(id_		)
          UpperCAmelCase__      :  List[Any]				      =			list(set(_lowerCAmelCase		)		)
          if len(_lowerCAmelCase		) == 0:
               raise ValueError("""At least one target must be provided when passed."""		)
          UpperCAmelCase__      :  int				      =			np.array(_lowerCAmelCase		)
          return target_ids




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase=None	,						_lowerCAmelCase=None		):
          UpperCAmelCase__      :  List[str]				      =			{}

          if targets is not None:
               UpperCAmelCase__      :  Optional[Any]				      =			self.get_target_ids(_lowerCAmelCase	,						_lowerCAmelCase		)
               UpperCAmelCase__      :  int				      =			target_ids

          if top_k is not None:
               UpperCAmelCase__      :  Dict				      =			top_k

          if self.tokenizer.mask_token_id is None:
               raise PipelineException(
                   """fill-mask"""	,						self.model.base_model_prefix	,						"""The tokenizer does not define a `mask_token`."""		)
          return {}, {}, postprocess_params




     def __call__(				self	,						_lowerCAmelCase	,						*_lowerCAmelCase	,						**_lowerCAmelCase		):
          UpperCAmelCase__      :  List[Any]				      =			super().__call__(_lowerCAmelCase	,						**_lowerCAmelCase		)
          if isinstance(_lowerCAmelCase	,						_lowerCAmelCase		) and len(_lowerCAmelCase		) == 1:
               return outputs[0]
          return outputs








import heapq
import sys

import numpy as np

lowerCamelCase_    :			Optional[Any]    =	tuple[int, int]

class 							_UpperCamelCase					:






			'''simple docstring'''

			def __init__(							self			:							Any   ):
							UpperCamelCase_:				Union[str, Any]						=    []
							UpperCamelCase_:				str						=    set()

			def    lowerCAmelCase__						(							self			:							List[Any]   ):
							if not self.empty():
											return self.elements[0][0]
							else:
											return float("""inf"""   )

			def    lowerCAmelCase__						(							self			:							List[Any]   ):
							return len(self.elements   ) == 0

			def    lowerCAmelCase__						(							self			:							Union[str, Any]    ,						snake_case_			:							int    ,						snake_case_			:							Dict   ):
							if item not in self.set:
											heapq.heappush(self.elements    ,						(priority, item)   )
											self.set.add(snake_case_   )
							else:
											# update
											# print("update", item)
											UpperCamelCase_:				Any						=    []
											((UpperCamelCase_), (UpperCamelCase_)):				Any						=    heapq.heappop(self.elements   )
											while x != item:
															temp.append((pri, x)   )
															((UpperCamelCase_), (UpperCamelCase_)):				str						=    heapq.heappop(self.elements   )
											temp.append((priority, item)   )
											for pro, xxx in temp:
															heapq.heappush(self.elements    ,						(pro, xxx)   )

			def    lowerCAmelCase__						(							self			:							Union[str, Any]    ,						snake_case_			:							Union[str, Any]   ):
							if item in self.set:
											self.set.remove(snake_case_   )
											UpperCamelCase_:				Dict						=    []
											((UpperCamelCase_), (UpperCamelCase_)):				Tuple						=    heapq.heappop(self.elements   )
											while x != item:
															temp.append((pro, x)   )
															((UpperCamelCase_), (UpperCamelCase_)):				Any						=    heapq.heappop(self.elements   )
											for prito, yyy in temp:
															heapq.heappush(self.elements    ,						(prito, yyy)   )

			def    lowerCAmelCase__						(							self			:							Optional[int]   ):
							return self.elements[0][1]




			def    lowerCAmelCase__						(							self			:							List[str]   ):
							((UpperCamelCase_), (UpperCamelCase_)):				Any						=    heapq.heappop(self.elements   )
							self.set.remove(snake_case_   )
							return (priority, item)







def 							A__       (    lowerCamelCase  ,     lowerCamelCase    )	->						str:
				# euclidean distance
				UpperCamelCase_:				Any						=    np.array(lowerCamelCase    )
				UpperCamelCase_:				Tuple						=    np.array(lowerCamelCase    )
				return np.linalg.norm(a - b    )







def 							A__       (    lowerCamelCase  ,     lowerCamelCase    )	->						List[str]:
				# integer division by time variable
				return consistent_heuristic(lowerCamelCase  ,     lowerCamelCase    ) // t







def 							A__       (    lowerCamelCase  ,     lowerCamelCase    )	->						Optional[Any]:
				# manhattan distance
				return abs(p[0] - goal[0]    ) + abs(p[1] - goal[1]    )







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )	->						Optional[int]:
				UpperCamelCase_:				Dict						=    g_function[start] + Wa * heuristics[i](lowerCamelCase  ,     lowerCamelCase    )
				return ans







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )	->						List[str]:
				UpperCamelCase_:				Any						=    np.chararray((n, n)    )
				for i in range(lowerCamelCase    ):
								for j in range(lowerCamelCase    ):
												UpperCamelCase_:				List[str]						=    """*"""

				for i in range(lowerCamelCase    ):
								for j in range(lowerCamelCase    ):
												if (j, (n - 1) - i) in blocks:
																UpperCamelCase_:				List[str]						=    """#"""

				UpperCamelCase_:				int						=    """-"""
				UpperCamelCase_:				Tuple						=    back_pointer[goal]
				while x != start:
								((UpperCamelCase_), (UpperCamelCase_)):				List[str]						=    x
								# print(x)
								UpperCamelCase_:				List[Any]						=    """-"""
								UpperCamelCase_:				List[str]						=    back_pointer[x]
				UpperCamelCase_:				Optional[Any]						=    """-"""

				for i in range(lowerCamelCase    ):
								for j in range(lowerCamelCase    ):
												if (i, j) == (0, n - 1):
																print(grid[i][j]  ,     end=""" """    )
																print("""<-- End position"""  ,     end=""" """    )
												else:
																print(grid[i][j]  ,     end=""" """    )
								print()
				print("""^"""    )
				print("""Start position"""    )
				print()
				print("""# is an obstacle"""    )
				print("""- is the path taken by algorithm"""    )
				print("""PATH TAKEN BY THE ALGORITHM IS:-"""    )
				UpperCamelCase_:				Optional[Any]						=    back_pointer[goal]
				while x != start:
								print(lowerCamelCase  ,     end=""" """    )
								UpperCamelCase_:				Any						=    back_pointer[x]
				print(lowerCamelCase    )
				sys.exit()







def 							A__       (    lowerCamelCase    )	->						Optional[Any]:
				if p[0] < 0 or p[0] > n - 1:
								return False
				if p[1] < 0 or p[1] > n - 1:
								return False
				return True







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     )	->						Any:
				for itera in range(lowerCamelCase    ):
								open_list[itera].remove_element(lowerCamelCase    )
				# print("s", s)
				# print("j", j)
				((UpperCamelCase_), (UpperCamelCase_)):				Optional[Any]						=    s
				UpperCamelCase_:				Dict						=    (x - 1, y)
				UpperCamelCase_:				Dict						=    (x + 1, y)
				UpperCamelCase_:				Any						=    (x, y + 1)
				UpperCamelCase_:				Union[str, Any]						=    (x, y - 1)

				for neighbours in [left, right, up, down]:
								if neighbours not in blocks:
												if valid(lowerCamelCase    ) and neighbours not in visited:
																# print("neighbour", neighbours)
																visited.add(lowerCamelCase    )
																UpperCamelCase_:				Dict						=    -1
																UpperCamelCase_:				Any						=    float("""inf"""    )

												if valid(lowerCamelCase    ) and g_function[neighbours] > g_function[s] + 1:
																UpperCamelCase_:				int						=    g_function[s] + 1
																UpperCamelCase_:				str						=    s
																if neighbours not in close_list_anchor:
																				open_list[0].put(lowerCamelCase  ,     key(lowerCamelCase  ,     0  ,     lowerCamelCase  ,     lowerCamelCase    )    )
																				if neighbours not in close_list_inad:
																								for var in range(1  ,     lowerCamelCase    ):
																												if key(lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    ) <= Wa * key(
																												    lowerCamelCase  ,     0  ,     lowerCamelCase  ,     lowerCamelCase    ):
																																open_list[j].put(
																																    lowerCamelCase  ,     key(lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )    )







def 							A__       (    )	->						int:
				UpperCamelCase_:				List[Any]						=    []
				for x in range(1  ,     5    ):
								for y in range(1  ,     6    ):
												some_list.append((x, y)    )

				for x in range(15  ,     20    ):
								some_list.append((x, 17)    )

				for x in range(10  ,     19    ):
								for y in range(1  ,     15    ):
												some_list.append((x, y)    )

    # L block
				for x in range(1  ,     4    ):
								for y in range(12  ,     19    ):
												some_list.append((x, y)    )
				for x in range(3  ,     13    ):
								for y in range(16  ,     19    ):
												some_list.append((x, y)    )
				return some_list


lowerCamelCase_    :			int    =	{0: consistent_heuristic, 1: heuristic_a, 2: heuristic_a}

lowerCamelCase_    :			List[str]    =	[
    (0, 1),
    (1, 1),
    (2, 1),
    (3, 1),
    (4, 1),
    (5, 1),
    (6, 1),
    (7, 1),
    (8, 1),
    (9, 1),
    (10, 1),
    (11, 1),
    (12, 1),
    (13, 1),
    (14, 1),
    (15, 1),
    (16, 1),
    (17, 1),
    (18, 1),
    (19, 1),
]
lowerCamelCase_    :			int    =	make_common_ground()


lowerCamelCase_    :			List[Any]    =	blocks_blk
# hyper parameters
lowerCamelCase_    :			Dict    =	1
lowerCamelCase_    :			Union[str, Any]    =	1
lowerCamelCase_    :			str    =	20
lowerCamelCase_    :			Any    =	3  # one consistent and two other inconsistent

# start and end destination
lowerCamelCase_    :			Optional[Any]    =	(0, 0)
lowerCamelCase_    :			int    =	(n - 1, n - 1)

lowerCamelCase_    :			Optional[Any]    =	1







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )	->						Optional[int]:
				UpperCamelCase_:				List[str]						=    {start: 0, goal: float("""inf"""    )}
				UpperCamelCase_:				Dict						=    {start: -1, goal: -1}
				UpperCamelCase_:				Any						=    []
				UpperCamelCase_:				List[str]						=    set()

				for i in range(lowerCamelCase    ):
								open_list.append(PriorityQueue()    )
								open_list[i].put(lowerCamelCase  ,     key(lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )    )

				UpperCamelCase_:				list[int]						=    []
				UpperCamelCase_:				list[int]						=    []
				while open_list[0].minkey() < float("""inf"""    ):
								for i in range(1  ,     lowerCamelCase    ):
												# print(open_list[0].minkey(), open_list[i].minkey())
												if open_list[i].minkey() <= Wa * open_list[0].minkey():
																global t
																t += 1
																if g_function[goal] <= open_list[i].minkey():
																				if g_function[goal] < float("""inf"""    ):
																								do_something(lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )
																else:
																				UpperCamelCase_, UpperCamelCase_:				Any						=    open_list[i].top_show()
																				visited.add(lowerCamelCase    )
																				expand_state(
																				    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     )
																				close_list_inad.append(lowerCamelCase    )
												else:
																if g_function[goal] <= open_list[0].minkey():
																				if g_function[goal] < float("""inf"""    ):
																								do_something(lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase    )
																else:
																				UpperCamelCase_:				Dict						=    open_list[0].top_show()
																				visited.add(lowerCamelCase    )
																				expand_state(
																				    lowerCamelCase  ,     0  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     )
																				close_list_anchor.append(lowerCamelCase    )
				print("""No path found to goal"""    )
				print()
				for i in range(n - 1  ,     -1  ,     -1    ):
								for j in range(lowerCamelCase    ):
												if (j, i) in blocks:
																print("""#"""  ,     end=""" """    )
												elif (j, i) in back_pointer:
																if (j, i) == (n - 1, n - 1):
																				print("""*"""  ,     end=""" """    )
																else:
																				print("""-"""  ,     end=""" """    )
												else:
																print("""*"""  ,     end=""" """    )
												if (j, i) == (n - 1, n - 1):
																print("""<-- End position"""  ,     end=""" """    )
								print()
				print("""^"""    )
				print("""Start position"""    )
				print()
				print("""# is an obstacle"""    )
				print("""- is the path taken by algorithm"""    )


if __name__ == "__main__":
							multi_a_star(start, goal, n_heuristic)






